JP2021518205A - Neurological examination system - Google Patents

Abstract

Systems and methods for assessing the anatomy of a subject's brain are provided. In one embodiment, the system for assessing the anatomy of a subject's brain comprises a computing device that communicates with a magnetic resonance imaging (MRI) device. The computing device determines the anatomical anomaly by comparing the test activation level within the shape of the anatomical structure with the data in the standard database, and the display device provides a graphical display of the anatomical anomaly. Can be operated to output. The test activation level is determined by aligning the functional magnetic resonance imaging (fMRI) data obtained by using an MRI device with the shape of the anatomical structure. The shape of the anatomical structure is contoured based on the segmentation of magnetic resonance (MR) data obtained by using an MRI device. Data in the standard database include activation levels of anatomical structures in multiple subjects who are not neurologically affected.

Description

[0001] The present disclosure relates generally to neurological examinations, and in particular to neurological examination systems and methods for identifying abnormalities in a subject's brain and determining the likelihood of neurological disorders.

[0002] Due to its high spatial resolution and excellent soft tissue contrast, Structural Magnetic Resonance Imaging (MRI) is well suited for the detection of brain and subcortical atrophy and the longitudinal tracking of white / gray matter changes. There is. MRI has a variety of variants and modalities that are useful in diagnosing neurological disorders.

[0003] Functional Magnetic Resonance Imaging (fMRI) is a variant of MRI that takes advantage of the magnetic properties of oxygenated and deoxygenated hemoglobin that result in various signal intensity values. The time course of the ratio of oxygenated blood to deoxygenated blood is used to generate images of task-related metabolic activity. Tasks to improve local blood-brain activity and ultimately local oxygen demand and blood flow are performed by subjects within an MRI scanner to study the cognitive function of individuals with neurological and psychiatric disorders. Used for.

[0004] Diffusion tensor imaging (DTI) is another MRI modality that utilizes the properties of water diffusion to provide information on the connectivity and functional integrity of brain tissue and underlined white matter tracts. DTI is based on the principle that water molecules diffuse along the principal axis of the tensor, which describes the local diffusion rate. The tensor is centered on a three-dimensional voxel and is visualized as an ellipsoid. As a result, voxels along the white matter pathway form diffusion lines, also known as fibrous pathways, when viewed along the long axis of the individual tensors. DTI tractography is an image processing technique that tracks such an ellipsoid along its long axis by starting from a user-defined seed point / region.

[0005] For example, electroencephalography (EEG) and magnetoencephalography (MEG) are used to study neurological disorders such as Alzheimer's disease, traumatic brain injury, and epilepsy. Both EEG and MEG measure ionic currents within neurons of the brain. The ionic current in a neuron is called the neuron current. EEG measures voltage fluctuations resulting from neuronal currents, while MEG measures magnetic fields induced by neuronal currents. Both EEG and MEG are used to assess brain activity by measuring neuronal currents. Therefore, the MEG and EEG data supplement the fMRI data as they evaluate various aspects of brain activity. The MEG and EEG data are also intercompared with the DTI data because the fibrous pathways of the DTI data are depictions of neuronal connectivity in the subject's brain.

[0006] Conventionally, fMRI data, DTI data, EEG data, and MEG data are usually analyzed separately for each region. Tracking and quantitative analysis of these data for anatomical units is not available. For the same reason, neither standard data nor biomarkers are created and developed in anatomical units. Therefore, there is a need for improved neurological examination systems and methods.

[0007] The embodiments of the present disclosure identify anatomically specific fMRI, DTI, EEG, and MEG data with data in a standard database to identify brain abnormalities in the subject and anatomically. It is configured to determine the likelihood of neurological disorders by comparing the unique fMRI, DTI, EEG, and MEG data with the data in the biomarker database. Data from the standard database include anatomically specific fMRI, DTI, EEG, and MEG data for healthy subjects who have not been diagnosed with neurological disorders, as well as the genomics, electronic medical records, and electronic medical records of these healthy subjects. Includes non-imaging data such as radiological reports. Data from the biomarker database include anatomically specific fMRI, DTI, EEG, and MEG data for subjects diagnosed with neurological disorders, as well as genomics and electronic medicine for those subjects with neurological disorders. Includes non-imaging data such as records, radiological reports.

[0008] Systems and methods for assessing the anatomy of a subject's brain are provided. In one embodiment, the system for assessing the anatomy of a subject's brain comprises a computing device that communicates with a magnetic resonance imaging (MRI) device. The computing device determines the anatomical anomaly by comparing the test activation level within the shape of the anatomical structure with the data in the standard database, and the display device provides a graphical display of the anatomical anomaly. Can be operated to output. The test activation level is determined by aligning the functional magnetic resonance imaging (fMRI) data obtained by using an MRI device with the shape of the anatomical structure. The shape of the anatomical structure is contoured based on the segmentation of magnetic resonance (MR) data obtained by using an MRI device. Data in the standard database include activation levels of anatomical structures in multiple subjects who are not neurologically affected.

[0009] In some embodiments, the computing device can further operate to determine the likelihood of neurological impairment by comparing the level of test activation associated with the anomaly with data from a biomarker database. be. Data from the biomarker database include activation levels of anatomical structures in multiple subjects who are neurologically affected. The graphical display includes the possibility of neurological disorders. In some embodiments, the computing device determines an anatomical anomaly by comparing the level of electrical activity of the examination within the shape of the anatomy with data in a standard database, and the examination associated with the anomaly. It can be further acted upon to determine the likelihood of neurological damage by comparing electrical activity levels with data from a biomarker database. Examination Electrical activity levels are determined by aligning the electroencephalography (EEG) data obtained by using an electroencephalography (EEG) device with the shape of the anatomical structure. The computing device communicates with the EEG device. Data in the standard database include electrical activity levels of the anatomical structure of multiple subjects who are not neurologically affected. Data from the biomarker database include electrical activity levels of the anatomical structure of multiple neurologically affected subjects.

[0010] In some embodiments, the computing device determines and is associated with anatomical anomalies by comparing examination neuronal activity levels within the shape of the anatomy with data from a standard database. It is possible to further act to determine the likelihood of neurological damage by comparing the neuronal activity level to the data in the biomarker database. Examination Neuron activity levels are determined by aligning the magnetoencephalography (MEG) data obtained by using a magnetoencephalography (MEG) device with the shape of the anatomical structure. The computing device communicates with the MEG device. Data in the standard database include neuronal activity levels of the anatomical structure of multiple subjects who are not neurologically affected. Data from the biomarker database include neuronal activity levels of the anatomical structure of multiple neurologically affected subjects.

[0011] In some embodiments, the computing device determines and is associated with anatomical anomalies by comparing the inspection fiber path density within the shape of the anatomy with data from a standard database. It is possible to further act to determine the likelihood of neurological damage by comparing the test fiber tract density to the data in the biomarker database. The inspection fiber path density is determined by aligning the diffusion tensor imaging (DTI) data obtained by using an MRI device with the shape of the anatomical structure. Data in the standard database include fibrous densities of anatomical structures in multiple subjects who are not neurologically affected. Data from the biomarker database include fiberway densities of anatomical structures in multiple neurologically affected subjects. In some embodiments, the graphical display includes treatment recommendations. In some embodiments, the graphical display includes prescription recommendations. In some embodiments, the graphical representation includes a report. In some embodiments, the system further includes an MRI device and a display device.

[0012] In another embodiment, the system for assessing the anatomy of a subject's brain comprises a computing device that communicates with a magnetic resonance imaging (MRI) device. The computing device determines the potential for neurological disability by comparing the level of test activation within the shape of the anatomical structure with the data in the biomarker database, and the display device has the potential for neurological disability. It can be operated to output a graphical display of. The test activation level is determined by aligning the functional magnetic resonance imaging (fMRI) data obtained by using an MRI device with the shape of the anatomical structure. The shape of the anatomical structure is contoured based on the segmentation of magnetic resonance (MR) data obtained by using an MRI device. Data from the biomarker database include activation levels of anatomical structures in multiple subjects who are neurologically affected.

[0013] In some embodiments, the computing device further operates to determine anatomical anomalies by comparing the level of examination activation within the shape of the anatomy with data from a standard database. It is possible. Data in the standard database include activation levels of anatomical structures in multiple subjects who are not neurologically affected. Graphical display includes anatomical abnormalities. In some embodiments, the computing device determines an anatomical structure abnormality by comparing the test electrical activity level within the shape of the anatomical structure with data in a standard database, and the test electrical activity level. Can be further acted upon to determine the likelihood of neurological disorders by comparing the data with the data in the biomarker database. Examination Electrical activity levels are determined by aligning the electroencephalography (EEG) data obtained by using an electroencephalography (EEG) device with the shape of the anatomical structure. The computing device communicates with the EEG device. Data in the standard database include electrical activity levels of the anatomical structure of multiple subjects who are not neurologically affected. Data from the biomarker database include electrical activity levels of the anatomical structure of multiple neurologically affected subjects.

[0014] In some embodiments, the computing device determines anatomical structure abnormalities by comparing the level of test neuron activity within the shape of the anatomy with data from a standard database and test neuron activity. It can be further acted upon to determine the likelihood of neurological disorders by comparing the levels with data from the biomarker database. Examination Neuron activity levels are determined by aligning the magnetoencephalography (MEG) data obtained by using a magnetoencephalography (MEG) device with the shape of the anatomical structure. The computing device communicates with the MEG device. Data in the standard database include neuronal activity levels of the anatomical structure of multiple subjects who are not neurologically affected. Data from the biomarker database include neuronal activity levels of the anatomical structure of multiple neurologically affected subjects.

[0015] In some embodiments, the computing device determines an anatomical structure abnormality by comparing the test fiber path density within the shape of the anatomical structure with data in a standard database and the test fiber path. It can be further acted upon to determine the likelihood of neurological damage by comparing the density with the data in the biomarker database. The inspection fiber path density is determined by aligning the diffusion tensor imaging (DTI) data obtained by using an MRI device with the shape of the anatomical structure. Data in the standard database include fibrous densities of anatomical structures in multiple subjects who are not neurologically affected. Data from the biomarker database include fiberway densities of anatomical structures in multiple neurologically affected subjects. In some embodiments, the graphical display includes treatment recommendations. In some cases, the graphical display includes prescription recommendations. In some embodiments, the graphical representation includes a report. In some embodiments, the system includes an MRI device and a display device.

[0016] Other devices, systems, and methods specifically configured to interface with and / or perform such methods are also provided.

[0017] Additional aspects, features, and advantages of the present disclosure will be apparent from the following detailed description, along with the drawings.

[0018] Aspects of the present disclosure are best understood from the following detailed description when read with the accompanying figures. It is emphasized that the various features are not drawn to a constant scale, according to industry standard practices. In fact, the dimensions of the various features are arbitrarily scaled up or down to clarify the discussion. In addition, the present disclosure may repeat reference numbers and / or letters in various examples. This repetition is for simplicity and clarity and does not, by itself, define the relationships between the various embodiments and / or configurations discussed.

[0019] FIG. 6 is a schematic representation of a neurological examination system according to aspects of the present disclosure. [0020] It is a flow chart which shows the method of constructing the standard database and the biomarker database for the analysis peculiar to an anatomical structure according to the aspect of this disclosure. [0021] FIG. 6 is a flow chart illustrating a method for determining the possibility of anatomical abnormalities and neurological disorders of a subject's brain according to aspects of the present disclosure. [0022] FIG. 6 is a schematic diagram showing a process flow for segmenting an MR image to outline the shape of an anatomical structure according to aspects of the present disclosure. 4 (410), 4 (420), 4 (430), 4 (440), 4 (450) and 4 (460) show black and white versions of the items shown in FIG. [0023] FIG. 6 shows a graphical representation of the activation level within the anatomy of the brain according to aspects of the present disclosure. [0024] It is a figure which shows the MR image of the brain of the subject which superposed the segmentation model of the amygdala hippocampal complex (AHC) of the subject by the aspect of this disclosure. [0025] It is a figure which shows the MR image of the brain of the subject which superposed the fiber path through the segmentation model of AHC of the subject by the aspect of this disclosure.

[0026] Next, in order to facilitate understanding of the principles of the present disclosure, the embodiments shown in the drawings are referred to and specific terms are used to describe the embodiments. Nevertheless, it will be appreciated that it is not intended to be limited to the scope of this disclosure. Any modifications and further modifications to the devices, systems, and methods described, as well as further applications of the principles of the present disclosure, are fully intended and within the present disclosure, as will usually be thought of by those skilled in the art in connection with the present disclosure. include.

[0027] Next, with reference to FIG. 1, a schematic diagram of the neurological examination system 100 according to some embodiments of the present disclosure is shown. The system 100 includes a magnetic resonance imaging (MRI) device 110, a magnetoencephalography (MEG) device 130, an electroencephalogram (EEG) device 140, a user input device 150, and a computing device 120 that telecommunications with the display 160. The computing device 120 includes processing circuits such as one or more processors that communicate with memory. Memory is a tangible computer-readable storage medium that stores instructions that can be executed by one or more processors. In some embodiments, the computing device 120 is a workstation or controller that acts as an interface between the MRI device 110, the MEG device 130, the EEG device 140, and the display 160 on the one hand. In some other embodiments, the computing device 120 controls only the MRI device 110. In these embodiments, the computing device 120 can access the data obtained by using the MEG device 130 and the EEG device 140, but does not directly control their operation. In some embodiments, the MRI device 110 operates and images in a variety of modalities, including, but not limited to, magnetic resonance (MR) imaging, diffusion tensor imaging (DTI), and functional magnetic resonance imaging (fMRI). The data is output to the computing device 120. In some embodiments, the MRI device 110 can operate simultaneously in various modality. For example, an MRI device can perform MR scans and DTI scans at the same time.

[0028] In some embodiments, the computing device 120 receives MR data from the MRI device 110, processes it, and outputs the MR image data to the display 160, so that the display 160 outputs the MR image. indicate. In some embodiments, the computing device 120 receives the fMRI data from the MRI device 110, processes it, and outputs the fMRI data to the display 160. In some embodiments, the computing device 120 aligns or realigns MR and fMRI data by appropriate processes such as survey scan, rigid body alignment, volume localization, and direction cosine. In some embodiments, acquisition of fMRI data does not end until a predetermined activation level or threshold activation level is achieved. In some embodiments, the computing device 120 receives data from the MEG 130, processes it, determines the neuron activity level, and outputs the neuron activity level to the display 160. Similarly, in some embodiments, the computing device 120 receives data from the EEG 140, processes it, determines the electrical activity level, and outputs the electrical activity level to the display 160. In some embodiments, the computing device 120 aligns or realigns MR data as well as EEG and MEG data by appropriate processes such as survey scan, rigid body alignment, volume localization, and direction cosine. In some embodiments, the EEG device 140 is compatible with the MRI device 110 and the EEG data is obtained at the same time as the MR scan. In these embodiments, the MR and EEG data must be aligned or reciprocally aligned under the same field of view. In some embodiments, the computing device 120 receives DTI data from the MRI device 110, processes it, identifies the fiber path, and outputs the identified fiber path to the display 160. In those embodiments, MR data and DTI data are obtained simultaneously or sequentially by MR scanning. If the subject being scanned remains stationary during the MR / DTI scan, the survey scan is sufficient to align the field of view of the MR scan with the field of view of the DTI scan. If the subject moves, additional survey scans may need to be performed to ensure proper alignment between the MR scan and the DTI scan. In some cases, the computing device 120 aligns or realigns MR data with DTI data through appropriate processes such as rigid body alignment, volume localization, and direction cosine.

[0029] In some embodiments, the MR data is a T1-weighted (T1 W) MR image, and the computing device 120 renders the MR image to outline the shape of the anatomical structure of the subject's brain. Automatically segment. In some embodiments, the computing device 120 segments MR image data based on a three-dimensional (3D) brain model. In some cases, the 3D brain model is received by the computing device 120 either from a storage medium or through a wired or wireless connection to a server or remote workstation. In some other cases, the 3D brain model is stored in a storage device within the computing device 120 or in a storage device searchable by the computing device 120. In some embodiments, the 3D brain model is a shape-constrained, deformable brain model. In some cases, the 3D brain model is Liu T. et al. , Shen D. , Ibanez L. et al. , Tao X. (Edit) MBIA 2011. Lecture Notes in Computer Science, vol. 7012. Multimodal Brain Image Analysis by Springer, Berlin, Heidelberg. In L. Zagorchev, C.I. Meyer, T.M. Stehle, R.M. Kneser, S, Young, and J. Young. It is a brain model described in "Evaluation of traumatic brain injury teaching a shape-constrained deformable model" by Weese, which is incorporated herein by reference in its entirety. In some embodiments, the 3D brain model is a deformable brain model described in US Pat. No. 9,256,951 entitled "SYSTEM FOR RAPID AND ACCURATE QUANTITATION OF TRAUMATIC BRAIN INJURY", or "METHOD AND". A shape-constrained deformable brain model described in US Patent Application Publication No. 20150146951 entitled "SYSTEM FOR QUANTATION OF IMAGE SEGMENTATION", each of which is incorporated herein by reference in its entirety.

[0030] In some embodiments, automatic segmentation not only outlines the shape of the anatomy of the brain, but also defines multiple voxels in each of the shapes. Aligning MR data with fMRI, DTI, EEG, and MEG data allows the shape and boxel to be transferred into the fMRI, DTI, EEG, MEG space, or fMRI, DTI, EEG, and MEG images. The MEG image can be superimposed on the MR image. In some embodiments, the computing device 120 determines the activation level within the voxel, based on the fMRI data from the MRI device 110. The activation level is a cumulative activation level, an instantaneous activation level, a time average activation level, or an event average activation level. Once the activation level of each voxel is known, the computing device 120 then determines the activation level within the shape of the anatomy by integrating the activation levels of all voxels within the shape. Can be done. In some embodiments, the computing device 120 has various activation levels, even if the activation level is an accumulation activation level, an instantaneous activation level, a time average activation level, or an event average activation level. Color coding can be used to represent. In some embodiments, the computing device 120 also outputs a contour of the activation level within the shape based on the activation level of the voxels within the shape. In some embodiments, the computing device 120 outputs a graphical display of the determined activation level within the shape to the display 160.

[0031] In some embodiments, MR and fMRI data include information about a number of shapes of various anatomical structures in the subject's brain. Given a task designed to increase local brain activity, activation levels within the shape of various anatomical structures can be sequenced or patterned over time. For example, a first high average activation level is observed within the first anatomical structure, and then a second high average activation level is observed within the second anatomical structure. The computing device 120 can also determine the sequence or pattern of activation between anatomical structures.

[0032] In some embodiments, based on DTI data from the MRI device 110, the computing device 120 identifies the fibrous pathway through the shape of the anatomical structure. In some embodiments, the computing device 120 determines the fibrous density within the shape of the anatomical structure. In some embodiments, the fiber path density comprises the ratio of the fiber path volume to the total product of the shape of the anatomical structure. In some embodiments, the computing device 120 uses color coding to represent different fiber path densities within different anatomical structures. In some embodiments, the computing device 120 outputs a graphical display of the fiber path density within the shape to the display 160. In some embodiments, the computing device 120 identifies the neuronal activity level within the shape of the anatomical structure, based on the MEG data from the MEG device 130. In some embodiments, the computing device 120 uses color coding to represent different levels of neuronal activity levels of different anatomical structures. In some embodiments, the computing device 120 outputs a graphical representation of the neuron activity level within the shape to the display 160. In some embodiments, based on EEG data from the EEG device 140, the computing device 120 identifies the level of electrical activity within the shape of the anatomical structure. In some embodiments, the computing device 120 uses color coding to represent different levels of electrical activity of different anatomical structures. In some embodiments, the computing device 120 outputs a graphical representation of the level of electrical activity within the shape to the display 160.

[0033] In some embodiments, the computing device 120 is used to build a standard database and a biomarker database. In these embodiments, the computing device 120 receives the diagnostic result of the subject. If the diagnosis is negative and indicates a healthy brain, the subject is identified as a neurologically unaffected subject and fMRI activation within each subject's anatomical structure. Levels, activation sequences, DTI fiber pathway densities, MEG neuron activity levels, and EEG electrical activity levels are stored by the computing device 120 in a standard database 170 that communicates with the computing device 120. However, if the diagnosis is positive and indicates a neurological disorder, the subject is identified as a neurologically affected subject, and the anatomical structure-specific data described above are available. It is stored in the biomarker database 180. Over time, standard databases include fMRI activation levels, activation sequences, DTI fiber tract densities, MEG neuron activity levels, within their respective anatomical structures for multiple subjects who are not neurologically affected. EEG electrical activity levels are included, and the biomarker database includes fMRI activation levels, activation sequences, and DTI fiber pathways within their respective anatomical structures for multiple neurologically affected subjects. Includes density, MEG neuron activity level, and EEG electrical activity level. In some embodiments, the computing device 120 is a standard database 170 and a biomarker based on the head size or head shape descriptor of a plurality of subjects who are not or neurologically affected. Normalize the data in database 180. As such, a more accurate dataset is prepared for comparison, taking into account variations due to head size.

[0034] In some embodiments, the computing device 120 has a diagnosed neurological disorder, fMRI activation level, activation sequence, DTI fiber tract density, MEG neuron activity level, and EEG electrical activity level. Is associated with the diagnostic result. All data stored in the biomarker database 180 are normalized for each anatomical structure, so that activity levels, neuronal activity levels, electrical activity levels, and DTI fiber densities are diagnosed neurological. Significantly quantified and analyzed for disability. The same cannot be said with the conventional use of the same data. For example, conventional DTI identifies fibrous pathways within a subject's brain. However, without a meaningfully defined space or shape, fiberway densities and fiberway volume ratios cannot be calculated or intercompared with corresponding values from various subjects. In some embodiments, the computing device 120 can statistically identify properties or biomarkers relating to activation levels, activation sequences, neuronal activity levels, electrical activity, and fiber path density.

[0035] In some embodiments, the computing device 120 comprises the subject's fMRI and DTI data from the MRI device 110, the subject's MEG data from the MEG device 130, and the subject from the EEG device 140. After receiving the EEG data, the computing device 120, in anatomical structural units, puts the subject's activation level, activation sequence, neuron activity level, electrical activity level, and fiberway density into the standard database 170. Compare with stored data. By such comparison, the computing device 120 identifies anomalies with respect to a particular anatomy. In some embodiments, after anomalies have been identified, the computing device 120 displays the subject's activation level, sequence of activation, neuronal activity level, electrical activity, and fibrous tract density within the anatomical structure. Is compared with the data stored in the biomarker database 180. The computing device 120 has "abnormal" anatomical activation levels, activation sequences, neuronal activity levels, electrical activity levels, and fibrous tract densities that are unique patterns or biomarkers of neurological disorders. Determine if it is consistent with and how likely it is to be an abnormality that indicates a neurological disorder. In an alternative configuration, the computing device 120 accesses both the standard database 170 and the biomarker database 180 in parallel in identifying anomalies and determining the potential for neurological disorders. In some embodiments, whenever the diagnosis is positive, information about treatment recommendations for the recommended therapy or treatment and prescription recommendations for the recommended drug is also stored in the biomarker database 180. In those embodiments, in addition to the potential for neurological disorders, the computing device 120 further determines treatment recommendations and prescription recommendations based on the recommended treatments and prescriptions in the biomarker database 180. Can be done. Due to the anatomical structure-specific properties of activation levels, activation sequences, neuronal activity levels, electrical activity levels, and fibrous tract densities obtained by the present disclosure, changes in brain activity can be tracked at the time of examination and across different examinations. Vertical tracking is possible.

[0036] In some embodiments, the computing device 120 produces a graphical display of identified abnormalities, potential neurological disorders, recommended treatments, and recommended formulations and outputs them to display 160. do. The graphical display includes color contours, text, pop-up dialog boxes, and clickable hyperlinks. In some embodiments, the graphical representation can be envisioned in the form of a report.

[0037] Next, with reference to FIG. 2, a flow diagram showing an exemplary method 200 for constructing a standard database and a biomarker database for anatomical structure-specific analysis is shown. Method 200 includes steps 202, 204, 206, 208, 210, 212, 214, 216A, and 216B. The steps of method 200 may be performed in a different order than that shown in FIG. 2, additional steps may be provided before, during, and after the steps, and / or of the steps described. It should be understood that some of the above may be replaced or removed in other embodiments. The process of method 200 is performed by a computing device of an MRI system, such as the computing device 120 of system 100. Method 200 will be described below with reference to FIGS. 3, 4, 5, 6, and 7.

[0038] In step 202 of method 200, MR data of the subject's brain is obtained by using an MRI device 110 that communicates with the computing device 120. The computing device 120 processes the MR data of the subject's brain, outputs the MR image data to the display 160, and displays an MR image such as the MR image 420 of FIG. In some embodiments, the MR data includes T1 W MR data. The MR image 420 shown in FIG. 4 is a top view of the subject's brain, but can one of ordinary skill in the art obtain an MR image of the subject's brain viewed from another direction by the computing device 120 as well? Or it will be understood that it can be derived. The MR data obtained in step 202 includes MR data of the anatomical structure of the subject's brain.

[0039] In step 204 of the method 200, the MR data of the subject's brain is the first shape of the first anatomy and the second shape of the second anatomy of the subject's brain. Segmented for contouring.

[0040] Next, with reference to FIG. 4, a process flow 400 for segmenting MR data to outline the shape of the anatomical structure of the subject's brain is shown. FIG. 4 was initially prepared as a color drawing because it is difficult to represent various aspects in black and white with a black and white medical image. At the time of filing this application, most patent offices around the world have not accepted color drawings. Therefore, to help show the embodiment shown in FIG. 4, additional figures FIG. 4 (410), 4 (420), 4 (430), 4 (440), 4 (450). ), And FIG. 4 (460), showing the various aspects previously shown in color in FIG. 4 in black and white. Differences between FIG. 4 and any of FIGS. 4 (410), 4 (420), 4 (430), 4 (440), 4 (450), and 4 (460) are original. A certain FIG. 4 should be interpreted with priority. The color version of FIG. 4 is available from the United States Patent and Trademark Office in the United States patent application related to this patent application.

[0041] In some embodiments, the computing device 120 segments MR data of the subject's brain represented by MR image 420 based on the 3D brain model 410. In some embodiments, the 3D brain model 410 is a shape-constrained, deformable brain model. In some cases, the 3D brain model 410 is a Liu T. , Shen D. , Ibanez L. et al. , Tao X. (Edit) MBIA 2011. Lecture Notes in Computer Science, vol. 7012. Multimodal Brain Image Analysis by Springer, Berlin, Heidelberg. In L. Zagorchev, C.I. Meyer, T.M. Stehle, R.M. Kneser, S, Young, and J. Young. It is a brain model described in "Evaluation of traumatic brain injury teaching a shape-constrained deformable model" by Weese, which is incorporated herein by reference in its entirety. In some cases, the 3D brain model is the deformable brain model described in US Pat. No. 9,256,951 entitled "SYSTEM FOR RAPID AND ACCURATE QUANTITATION OF TRAUMATIC BRAIN INJURY", or "METHOD AND". A shape-constrained deformable brain model described in US Patent Application Publication No. 20150146951 entitled "FOR QUANTITAIVE EVALUATION OF IMAGE SEGMENTATION", each of which is incorporated herein by reference in its entirety. In some embodiments, the 3D brain model 410 is stored in a computing device 120, or a storage device or medium searchable by the computing device 120. Step 204 may be performed at the same time as step 202, or may be performed after step 202.

[0042] As shown in MR image 430, the 3D brain model 410 is initialized by matching it with the MR image 420 of the brain. A generalized Hough transform (GHT) is then performed on the 3D brain model 410 to match the 3D brain model 410 to the shape of the anatomical structure of the MR image 420 in terms of location and orientation, as shown in MR image 440. Let me. The 3D brain model 410 then undergoes parameter adaptation, as shown in MR image 450. Location, orientation, and magnification changes are adjusted using global similarity transformations and / or multiple linear transformations to better fit the anatomy of MR image 420. As shown by MR image 460, the 3D brain model 410 undergoes a deformable fit. Numerous iterations of boundary detection and mesh adjustment of the 3D brain model 410 are performed to fit the 3D brain model to the anatomy of the brain.

[0043] In step 206 of method 200, fMRI data of the subject's brain is obtained. fMRI relies on the fact that oxygenated and deoxygenated hemoglobin have different magnetic properties that result in different magnetic resonance (MR) signal intensities. Since cerebral blood flow is directly correlated with neuronal activation, fMRI measures the activation level of that brain region by measuring blood demand in that region. In addition, fMRI is also a tool and technique for measuring oxygen demand in brain regions, as blood demand represents oxygen demand. During an fMRI scan, the subject is given a task designed to increase local brain activity, and the MRI device detects changes in the ratio of oxygenated blood to deoxygenated blood. Step 206 may be performed at the same time as steps 202 and 204, or may follow them.

[0044] For example, the task is a double N-back task. In the dual N-back task, the subject is presented with a series of visual and auditory stimuli at the same time. In some embodiments, the subject is required to start with a one-back condition and provide an acknowledgment if the current visual stimulus matches the previous visual stimulus. Similarly, subjects are required to provide an acknowledgment if the current auditory stimulus matches the previous auditory stimulus. If both the current visual and auditory stimuli match the previous visual and auditory stimuli, the subject is asked to provide a double acknowledgment. If none of the stimuli match, no response is required. When the correct answer rate of the subject reaches a certain level, the n-back level is increased by 1 (for example, from 1 back to 2 back). If the accuracy level falls below a certain level, the n-back level is reduced by 1 (eg, from 3 backs to 2 backs). In some cases, the n-back level remains the same if the subject's accuracy level is maintained at a constant level. The dual N-back task is Susanne M.D. Jaeggi et al., Improving Fluid Intelligence with Training on Working Memory, Pro. Natl. Acad. Sc. U.S. S. A. May 13, 2008; 105 (19): 6829-6833. FIG. 4 shows the brain activation level of a control subject and the brain activation level of a subject with mild traumatic brain injury (MTBI) when the subject underwent a double N-back task. Without anatomical-specific activation levels, activation levels within a particular anatomical structure cannot be quantified and meaningfully associated with a particular neurological disorder. The systems and methods of the present disclosure do just that. By segmenting the MR data and aligning the MR data with the fMRI data, the activation level in each of the shapes of the anatomical structure is determined. The activation levels used herein are cumulative activation levels, instantaneous activation levels, time average activation levels, or event average activation levels.

Step 206 will be described in connection with FIG. FIG. 5 shows an MR image 500 of the subject's brain overlaid with emphasized boundaries of the shape of the anatomical structure, including the shapes of the thalamus 510 and the corpus callosum 520. In some embodiments, the computing device 120 has a first activation level within a first shape (eg, the shape of the thalamus 510), and a second shape (eg, the shape of the corpus callosum 520). Determine the second activation level. As shown in FIG. 5, the first activation level is represented by the first graphical overlay 610 and the second activation level is represented by the second graphical overlay 620. Here, the first and second activation levels are cumulative activation level, instantaneous activation level, time average activation level, or event average activation level. In addition, the computing device 120 determines patterns or sequences of activation of various anatomical structures. For example, the first activation level of the thalamus 510 shape is increasing, while the second activation level of the corpus callosum shape is increasing. The second activation level then increases in response to the dual N-back task, while the first activation level decreases in response to the same task. In addition to the quantitative intensity of activation levels, patterns / sequences of activation between various anatomical structures in response to tasks or stimuli also indicate neurological disorders or symptoms.

[0046] In step 208 of method 200, EEG data of the subject's brain is obtained. By segmenting the MR data and aligning the MR data with the EEG data, the level of electrical activity within each of the shapes of the anatomical structure is determined. Step 208 may be performed simultaneously with steps 202, 204, and 206, or may be performed following them.

[0047] In step 210 of method 200, MEG data of the subject's brain is obtained. By segmenting the MR data and aligning the MR data with the MEG data, the level of neuronal activity within each of the shapes of the anatomical structure is determined.

[0048] In step 212 of Method 200, DTI data of the subject's brain is obtained. By segmenting the MR data and aligning the MR data with the DTI data, the computing device 120 identifies the fibrous pathways through the anatomical structure and the fiber path density or fibrous path volume within the anatomical structure. Determine the ratio of. Step 212 is shown in connection with FIGS. 6 and 7. One of the ways in which the computing device 120 segments anatomical structures is by representing the anatomical structures in voxels. An exemplary voxel representation is shown in FIG. 6, where the subject's MR image 600 includes a segmented representation 610 of the subject's amygdala hippocampal complex (AHC). Segmented representation 610 includes voxels that fill the subject's AHC shape. Although FIG. 6 shows the subject's AHC segmented representation 610, one of ordinary skill in the art will appreciate that such segmentation can be applied to any brain anatomy. Next, referring to FIG. 7, an MR image 700 of the subject's brain is shown in which the fiber path 710 via the segmented representation 610 of the subject's AHC is superimposed. In some embodiments, the voxels in the segmented representation 610 serve as a starting point or "seed" for tracking the fibrous tracts 710 through them, thereby identifying the fibrous tracts 710 in step 212. can. Step 212 may be performed simultaneously with steps 202, 204, 206, and 208, or may be performed following them.

In step 214 of method 200, activation levels, activation sequences, electrical activity levels, neuronal activity levels, and fibrous tract densities are associated with the diagnosis of the subject's brain. In the context of system 100 shown in FIG. 1, in step 214, the computing device 120 receives the patient's diagnostic results for the brain. If the diagnostic result is negative and indicates a healthy brain, the computing device 120 will have an fMRI activation level, activation sequence, DTI fiber tract density, MEG neurons within each anatomical structure of the brain. Activity levels, EEG electrical activity levels are associated with negative diagnostic results or neurologically unaffected subjects. However, if the diagnosis is positive and indicates a neurological disorder, the computing device 120 associates anatomical structure-specific data with the neurologically affected subject. In some embodiments, whenever the diagnosis is positive, information on treatment recommendations for the recommended therapy or treatment and prescribing recommendations for the recommended drug is further diagnosed as a neurological disorder. Associated with the target person.

[0050] The method 200 then branches into step 216A and step 216B. In step 216A, fMRI activation levels, activation sequences, DTI fiber tract densities, MEG neuron activity levels, within each anatomical structure associated with negative diagnostic results or neurologically unaffected subjects, The EEG electrical activity level is stored in a standard database such as the standard database 170 shown in FIG. Over time, standard databases include fMRI activation levels, activation sequences, DTI fibrous pathways, MEG neuron activity levels, and EEG within their respective anatomical structures for multiple subjects who are not neurologically affected. Includes electrical activity levels. In step 216B, fMRI activation levels, activation sequences, DTI fiber tract densities, MEG neuron activity levels, EEG electrical activity levels within each anatomical structure associated with the neurologically affected subject. Is stored in a biomarker database such as the biomarker database 180 shown in FIG. Over time, the biomarker database contains fMRI activation levels, activation sequences, DTI fiber tract densities, and MEG neuron activity levels within their respective anatomical structures for multiple neurologically affected subjects. , EEG electrical activity levels are included. In cases where treatment recommendations and / or prescription recommendations are associated with neurologically affected subjects diagnosed with neurological disorders, treatment recommendations and prescription recommendations are also stored in the biomarker database 180. For example, a subject diagnosed with epilepsy is prescribed an antiepileptic drug. Their treatment and / or prescribing recommendations include their associated imaging (such as fMRI, EEG, MEG, and DTI), non-imaging data (genomics, clinical essays, electronic medical records, radiological reports, etc.), and / Alternatively, it is stored in the database along with previous treatment recommendations. In some embodiments, the data in the standard database and the biomarker database are categorized based on age, gender, race, or a combination thereof.

[0051] Next, with reference to FIG. 3, a method 300 for determining the possibility of anatomical abnormalities and neurological disorders of the subject's brain is shown. Method 300 includes steps 302, 304, 306, 308, 310, 312, 314, 316, and 318. The steps of method 300 may be performed in a different order than that shown in FIG. 3, additional steps may be provided before, during, and after the steps, and / or of the steps described. It should be understood that some of these may be replaced or removed in other embodiments. The process of method 300 is performed by a computing device of an MRI system, such as the computing device 120 of system 100. Method 300 will be described below with reference to FIGS. 3, 4, 5, 6, and 7. Steps 302, 304, 306, 308, 310, and 312 of method 300 are similar to steps 202, 204, 206, 208, 210, and 212 of method 200 and will not be described in great detail below.

[0052] In step 302 of method 300, MR data of the subject's brain is obtained by using an MRI device 110 that communicates with the computing device 120.

[0053] In step 304 of the method 300, the MR data of the subject's brain is obtained from the first shape of the first anatomical structure and the second shape of the second anatomical structure of the subject's brain. Segmented for contouring.

[0054] In step 306 of Method 300, fMRI data of the subject's brain is obtained. The fMRI data is aligned with the MR data by survey scans or by appropriate alignment processes such as volume localization and direction cosine. By segmenting the MR data and aligning the MR data with the fMRI data, the level of test activation in each of the anatomical shapes is determined. The test activation levels used herein are cumulative activation levels, instantaneous activation levels, time average activation levels, or event average activation levels.

[0055] In step 308 of method 300, EEG data of the subject's brain is obtained. EEG data is aligned with MR data by appropriate alignment processes such as survey scans, rigid body alignment, volume localization, and direction cosine. By segmenting the MR data and aligning the MR data with the EEG data, the level of electrical activity to be examined within each of the shapes of the anatomical structure is determined.

[0056] In step 310 of method 300, MEG data of the subject's brain is obtained. The MEG data is aligned with the MR data by appropriate alignment processes such as survey scans, rigid body alignment, volume localization, and direction cosine. By segmenting the MR data and aligning the MR data with the MEG data, the level of laboratory neuron activity within each of the shapes of the anatomical structure is determined.

[0057] In step 312 of method 300, DTI data of the subject's brain is obtained. DTI data is aligned with MR data by survey scan or by appropriate alignment processes such as survey scan, rigid body alignment, volume localization, and direction cosine. By segmenting the MR data and aligning the MR data with the DTI data, the computing device 120 identifies the fibrous pathways through the anatomical structure and determines the fibrous pathway density within the anatomical structure.

[0058] In step 314 of method 300, anatomical abnormalities are found in the computing device 120, such as test activation level, test electrical activity level, test neuron activity level, and test fiber pathway density in standard database 170, etc. Determined by comparison with data from the standard database of. As mentioned above for step 216A of Method 200, the standard database contains fMRI activation levels, activation sequences, DTI fibers within each anatomical structure for multiple subjects who are not neurologically affected. Includes tract density, MEG neuron activity levels, and EEG electrical activity levels. In some embodiments, the data in the standard database is normalized based on the subject's age, social gender, gender, and / or head size, followed by the subject's test activation level, test electrical. Compared to activity level, test neuron activity level, and test fiber tract density. In some embodiments, the subject's test activation level, test electrical activity level, test neuron activity level, and test fiber pathway density within the anatomical structure deviate from standard values by a threshold percentage. , Anatomical abnormalities are determined. In some embodiments, the threshold percentage is a percentage determined based on a cross-comparison between data in the standard database and data in the biomarker database. In some embodiments, the threshold percentage is part of the standard deviation of the normal form data.

[0059] In step 316 of Method 300, the potential for neurological disorders is associated with abnormalities in test activation levels, test electrical activity levels, test neuron activity levels, and test fiber tract densities as biomarker database data. Determined by comparing with. As mentioned above for step 216B of Method 200, the biomarker database contains fMRI activation levels, activation sequences, DTIs within their respective anatomical structures for multiple neurologically affected subjects. Fiber pathway density, MEG neuron activity level, EEG electrical activity level are included. In some embodiments, the data in the biomarker database is normalized based on the subject's age, social gender, gender, and / or head size, followed by the subject's test activation level, test electricity. Compared to target activity level, test neuron activity level, and test fiber tract density. In some embodiments, the potential for neurological disorders is a biomarker database of test activation levels, test electrical activity levels, test neuron activity levels, and test fiber tract densities of subjects within the anatomical structure. Determined by matching with biomarkers of neurological disorders. For example, a biomarker that a neurologically affected subject with X activation levels and Y neuronal activity levels in anatomical structure Z has a 95% chance of being diagnosed with neurological disorder A. If the data in the database show statistically and the test data of the subject matches or exceeds the X activation level and Y neuron activity level of anatomical structure Z, the subject's possibility of neurological disorder A is It is 95%. In some embodiments, the data in the biomarker database is compared with the data in the standard database to generate a threshold percentage for determining anomalies in step 314. For example, if the average activation level in the anatomical structure in the biomarker database is 15% higher than the corresponding one in the standard database, 15% is for the purpose of determining anomalies in the anatomical structure in step 314. Serves as a threshold ratio for.

[0060] In some embodiments, steps 314 and 316 are performed in parallel and independently of each other. In those embodiments, the computing device 120 accesses both databases at the same time and performs the comparisons required in steps 314 and 316 separately. In some other embodiments, steps 314 and 316 are performed in sequence, and step 316 depends on the results of step 314. In these embodiments, after the anomaly has been identified for the anatomical structure, step 316 is performed only for that "abnormal" anatomical structure to produce a convergence result.

[0061] In step 318 of the method 300, a graphical display of the possibility of an abnormality or neurological disorder is output to a display such as the display 160. As mentioned above, the biomarker database contains information on treatment recommendations for recommended therapies or treatments and prescribing recommendations for recommended drugs. In those embodiments, the graphical representation may further include treatment and prescribing recommendations for the neurological disorder if the likelihood of the neurological disorder is greater than 0%. In some other embodiments, the graphical representation only includes treatment and prescribing recommendations for neurological disorders when the likelihood of neurological disorders is greater than 50%. In some embodiments, the graphical display includes color contours, text, pop-up dialog boxes, and clickable hyperlinks. In some embodiments, the graphical representation can envision the form of a radiological report.

[0062] The systems, devices, and methods of the present disclosure are described in US Patent Provisional Application Atty. Dkt. No. Includes the features described in 2017PF02586 / 44755.1862PV01, which are incorporated herein by reference in their entirety.

Those skilled in the art will recognize that the devices, systems, and methods described above may be modified in various ways. As a result, those skilled in the art will appreciate that the embodiments included in the present disclosure are not limited to the particular exemplary embodiments described above. In that regard, exemplary embodiments are illustrated and described, but extensive modifications, modifications, and substitutions are intended in the aforementioned disclosure. It will be appreciated that such modifications may be made to those described above without departing from the scope of the present disclosure. As a result, it is appropriate that the appended claims be broadly construed in a manner consistent with the present disclosure.

Claims (18)

A system for evaluating the anatomical structure of a subject's brain.
The computing device includes a computing device that communicates with a magnetic resonance imaging (MRI) device.
Determining an abnormality of the anatomical structure by comparing the test activation level within the shape of the anatomical structure with data in a standard database, wherein the test activation level is the use of the MRI device. Determined by aligning the functional magnetic resonance imaging (fMRI) data obtained with the anatomical structure with the shape of the anatomical structure obtained by using the MRI device. Determining anatomical anomalies that are contoured based on magnetic resonance imaging (MR) data segmentation,
It can operate to output a graphical display of the anatomical anomaly to the display device.
The data in the standard database include activation levels of anatomical structures in multiple subjects who are not neurologically affected.
system. The computing device
It can be further acted upon to determine the likelihood of neurological impairment by comparing the test activation level associated with the abnormality with data from a biomarker database.
Data from the biomarker database include activation levels of said anatomical structures in multiple neurologically affected subjects.
The graphical representation includes the possibility of the neurological disorder.
The system according to claim 1. The computing device
Determining an abnormality in the anatomical structure by comparing the test electrical activity level within the shape of the anatomical structure with data in the standard database, wherein the test electrical activity level is an electroencephalogram. Determining an abnormality of the anatomical structure, which is determined by aligning the EEG data obtained by using an examination (EEG) device with the shape of the anatomical structure.
It is further operable to determine the likelihood of the neurological disorder by comparing the test electrical activity level associated with the anatomical abnormality with the data in the biomarker database. ,
The computing device communicates with the EEG device and
The data in the standard database includes the electrical activity levels of the anatomical structure of the neurologically unaffected subjects.
The data in the biomarker database comprises the level of electrical activity of the anatomical structure of the neurologically affected subject.
The system according to claim 2. The computing device
Determining an abnormality of the anatomical structure by comparing the test neuron activity level within the shape of the anatomical structure with data in the standard database, wherein the test neuron activity level is a magnetoencephalogram. Determining an abnormality of the anatomical structure, which is determined by aligning the MEG data obtained by using an examination (MEG) device with the shape of the anatomical structure.
It is further operable to determine the likelihood of the neurological disorder by comparing the test neuron activity level associated with the anatomical abnormality with the data in the biomarker database.
The computing device communicates with the MEG device and
Data from the standard database include neuronal activity levels of the anatomical structure of the neurologically unaffected subjects.
Data from the biomarker database include neuronal activity levels of the anatomical structure of the neurologically affected subject.
The system according to claim 2. The computing device
Determining an abnormality of the anatomical structure by comparing the test fiber path density within the shape of the anatomical structure with data in the standard database, wherein the test fiber path density is the MRI device. Determining the anatomical anomaly, which is determined by aligning the diffusion tensor imaging (DTI) data obtained by the use of the anatomical structure with the shape of the anatomical structure.
It is further operable to determine the likelihood of the neurological disorder by comparing the test fiber tract density associated with the anatomical abnormality with the data in the biomarker database. ,
The data in the standard database included the fibrous density of the anatomical structure of the neurologically unaffected subjects.
The data in the biomarker database includes the fibrous density of the anatomical structure of the neurologically affected subject.
The system according to claim 2. The system of claim 2, wherein the graphical display includes treatment recommendations. The system of claim 2, wherein the graphical display includes prescription recommendations. The system of claim 2, wherein the graphical representation includes a report. The system according to claim 2, further comprising the MRI device and the display device. A system for evaluating the anatomical structure of a subject's brain.
The computing device includes a computing device that communicates with a magnetic resonance imaging (MRI) device.
Determining the likelihood of neurological damage by comparing the level of test activation within the shape of the anatomical structure with data from a biomarker database, wherein the level of test activation is that of the MRI device. The shape of the anatomical structure is determined by aligning the functional magnetic resonance imaging (fMRI) data obtained by use with the shape of the anatomical structure, and the shape of the anatomical structure is obtained by use of the MRI device. Determining the potential for neurological disorders, contoured based on the segmentation of magnetic resonance imaging (MR) data
It can be actuated to output a graphical display of said possibility of said neurological disorder to a display device.
Data from the biomarker database include activation levels of said anatomical structures in multiple neurologically affected subjects.
system. The computing device
It is possible to further act to determine anomalies in the anatomy by comparing the test activation level within the shape of the anatomy with data in a standard database.
Data from the standard database include activation levels of said anatomical structures in multiple subjects who are not neurologically affected.
The graphical representation comprises an abnormality in the anatomical structure.
The system according to claim 10. The computing device
Determining an abnormality in the anatomical structure by comparing the test electrical activity level within the shape of the anatomical structure with data in the standard database, wherein the test electrical activity level is an electroencephalogram. Determining an abnormality of the anatomical structure, which is determined by aligning the EEG data obtained by using an examination (EEG) device with the shape of the anatomical structure.
It is further operational to determine the likelihood of the neurological disorder by comparing the test electrical activity level with the data in the biomarker database.
The computing device communicates with the EEG device and
The data in the standard database includes the electrical activity levels of the anatomical structure of the neurologically unaffected subjects.
The data in the biomarker database comprises the level of electrical activity of the anatomical structure of the neurologically affected subject.
The system according to claim 11. The computing device
Determining an abnormality of the anatomical structure by comparing the test neuron activity level within the shape of the anatomical structure with data in the standard database, wherein the test neuron activity level is a magnetoencephalogram. Determining the anomaly of the anatomical structure, which is determined by aligning the MEG data obtained by using the examination (MEG) device with the shape of the anatomical structure.
It can be further acted upon to determine the likelihood of the neurological disorder by comparing the test neuron activity level with the data in the biomarker database.
The computing device communicates with the MEG device and
Data from the standard database include neuronal activity levels of the anatomical structure of the neurologically unaffected subjects.
Data from the biomarker database include neuronal activity levels of the anatomical structure of the neurologically affected subject.
The system according to claim 11. The computing device
Determining an abnormality of the anatomical structure by comparing the test fiber path density within the shape of the anatomical structure with data in the standard database, wherein the test fiber path density is the MRI device. Determining the anatomical anomaly, which is determined by aligning the diffusion tensor imaging (DTI) data obtained by the use of the anatomical structure with the shape of the anatomical structure.
It can be further actuated to determine the likelihood of the neurological disorder by comparing the test fiber tract density with the data in the biomarker database.
The data in the standard database included the fibrous density of the anatomical structure of the neurologically unaffected subjects.
The data in the biomarker database includes the fibrous density of the anatomical structure of the neurologically affected subject.
The system according to claim 11. 10. The system of claim 10, wherein the graphical display includes treatment recommendations. 10. The system of claim 10, wherein the graphical display includes prescription recommendations. The system of claim 10, wherein the graphical representation comprises a report. The system according to claim 10, further comprising the MRI device and the display device.

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